Dynamic emission discharge reduction

ABSTRACT

A method for establishing and using a pollutant emission scavenging forecasting (PESF) model to calculate a purgeable pollutant emission to dynamically control an emitted pollutant at any target concentration. The dynamic control considers the initial pollutant concentration and pollutant scavenging ability of atmosphere. The method further takes into account the constraint conditions specified by a user, and employs a dynamic emission correction system to quickly calculate an optimal pollutant emission scheme. If the emission is lower than minimum acceptable value for a current time instance (a moment), the method corrects the emission intensity before (prior to) this moment by changing the initial concentration at this time. This initial concentration is the final pollutant concentration of a prior moment. Since the method makes full use of the atmospheric pollutants scavenging ability, the dynamic emission control scheme can provide the most effective and lowest economic losses solution.

FIELD

Various embodiments of the present invention relate to systems andmethods for avoiding pollution in an area, e.g., an urban area, andparticularly, to a system and method for dynamic control of emissionsdischarge in an area under target pollution concentration andmeteorological and chemical constraint conditions.

BACKGROUND

The impact of air pollution, e.g., in urban environments is an importantissue due to both acute and chronic effects on human health. Present dayurban environments are mostly dominated by traffic emissions, e.g.,chemically transformed hydrocarbons such as emitted by motor vehicles.For example, main traffic-related pollutants are CO, NOx, SO₂;hydrocarbons, and particles. Combustion also produces a mixture of NO2and NO. Other pollutants (emissions) include particulate matter (e.g.,PM less than 2.5 microns (PM2.5) and less than 10 microns (PM10)) andvolatile organic compounds (VOCs).

In an example “local” (spatial) area or region, concentration of thepollutants is influenced by transmission, diffusion and emissionprocesses happening in the atmosphere.

SUMMARY

One embodiment of the present invention provides a computer-implementedmethod of dynamically controlling emission discharge of pollutants by apollutant emitting source in a spatial area. The computer-implementedmethod comprises: receiving, at a processor, geographic data,atmospheric meteorological data and chemical data pertaining to thespatial area; receiving at the processor, emissions data representingpollutant substances emitted by a emitting source; generating, by theprocessor, based on the received meteorological and emissions data, apollutant scavenging ability factor representing a pollutantcomprehensive scavenging ability of the atmosphere; computing, at theprocessor, a purgeable pollutant emission value over a period of timeusing said scavenging ability factor; receiving, at the processor, oneor more emissions constraint conditions; and dynamically controllingemissions at the emitting source based on the purgeable pollutantemission value, wherein controlled pollutant emissions of the emittingsource comply with the emission constraint conditions.

Other embodiments include a computer-implemented system and a computerprogram product.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Through the more detailed description of some embodiments of the presentinvention in the accompanying drawings, the above and other objects,features and advantages of the present invention will become moreapparent, wherein the same reference generally refers to the samecomponents in the embodiments of the present invention.

FIG. 1 illustrates an exemplary network in accordance with one or moreembodiments of the present invention

FIG. 2 shows an example of a dynamic emission discharge scheme andcustomized system according to one or more embodiments of the presentinvention;

FIG. 3 shows an example method for generating an emission dischargescheme according to one or more embodiments of the present invention;

FIG. 4 shows an example method for generating coefficients based onnumerical sensitive experiments in one or more embodiments of thepresent invention;

FIG. 5 depicts a graph of example emissions amounts (e.g., true amounts)periodically over a time period as compared to purgeable emissionamounts at corresponding time instances in one embodiment;

FIG. 6 shows a time line depicting the use of the PESF model and keycoefficients to determine the purgeable emissions component E_(t) atsuccessive times t=i−1, i, i+1 in one embodiment;

FIG. 7 shows an example method for generating an emission correctionscheme according to one or more embodiments of the present invention;

FIG. 8 depicts a further time line corresponding to the timeline of FIG.6 however representing optimal emissions scheme showing modifiedpurgeable emission components E′^(t) computed at the successive timest=i−1, i, i+1 in one embodiment;

FIG. 9A shows an example case study depicting a line plot of theparticulate matter PM2.5 emission concentration emitted by a source ofthe pollutant compared with a plot of the computed PM2.5 concentrationwith purgeable pollutant emission E^(t) computed at the same timeinstances;

FIG. 9B shows for the example case study of FIG. 9A, a bar graphplotting the example corresponding daily true emission levels emitted bythe source of the pollutant compared with the purgeable pollutantemission levels E^(t) computed at the same time instances;

FIG. 9C shows for the example case study of FIG. 9A, a line plotdepicting the particulate matter PM2.5 emission concentration emittedusing an optimal emission scheme described herein in connection withFIG. 7 as compared with the plot of the computed PM2.5 concentrationwith purgeable pollutant emission E^(t); and

FIG. 9D shows for the example plot of FIG. 9C, a bar graph plotting theoptimal emission levels emitted by the source of the pollutant at thesuccessive time instances to achieve the optimal concentration levels ascompared with the computed purgeable pollutant emission levels E^(t)computed at the same time instances.

DETAILED DESCRIPTION

It is to be understood that the present invention can be implemented invarious manners, and thus should not be construed to be limited to theembodiments disclosed herein. On the contrary, the below-describedembodiments are provided to facilitate understanding of the presentinvention, and convey the scope of the present invention to thoseskilled in the art.

As human activities discharge emissions into the atmosphere, part of theemissions can be removed by meteorological condition, e.g., such bybeing blown away by wind, or chemical reaction in atmosphere. Thisremoved part is referred to herein as purgeable emissions.

By way of overview, one or more embodiments of the present invention aredirected to a system, method and computer program product to identifypurgeable pollutant emissions under different atmospheric conditions andto generate a dynamic short-term emission plan for a source of operatingdevices and equipment known to emit pollutants. For example, ashort-term emission plan may have a duration ranging from 1 day (e.g., anext day) to two weeks, and may be generated for an entity such as agovernment of the local area, or any entity that will make a dischargecut plan based on model outputs. Further, some embodiments provide for acustomized emission-reduction scheme for different demands. Oneend-result/goal is to increase emission-reduction efficiency, andconsequently reduce economic and other losses. In some embodiments, theemission discharge/reduce/control plan of the present invention modelsthe emission volume discharged over multiple day(s). Since the volumecan be different each day, in this case, a dynamic emissions controlmodel may update the plan daily. In one embodiment, the referred todemands may include one or more specific pollutant target concentrations(e.g., keep PM_(2.5) on 75 ug/m³ or 100 ug/m³?), or other varyingconstraints, e.g., different customer's require different “lowest cupercentage”, which will lead to different emission-reduction plans. Oneor more embodiments provide different emission reduction plans based ondifferent demands for different customer goals and constraints.

FIG. 2 shows an example of a dynamic emission discharge scheme andcustomized system according to one or more embodiments of the presentinvention. As shown in FIG. 2, at 74, a computer system softwarecomponent computes a P-factor which represents a pollutant comprehensivescavenging ability of the atmosphere. In one embodiment, the computingof the P-factor at 74 takes into account meteorological scavengingability 72 and chemical scavenging ability 76. In one embodiment, theP-factor is computed that includes the chemical scavenging ability(e.g., photochemical reactions and aerosol condensation in atmosphere)and meteorological scavenging ability (e.g., wind, solar radiation, anddeposition processes in atmosphere) thereby reflecting a pollutantcomprehensive scavenging ability of the atmosphere in a target spatialarea, e.g., a city. That is, the “P-factor,” is a parameter thatrepresents the pollutant comprehensive scavenging ability of atmosphere,including meteorological scavenging and chemical scavenging ability.

The computing of P-factor at 74 is computed by first by representing thepollutant concentration C_(F) at any time in the atmosphere according toequation 1) as follows:

$\begin{matrix}{C_{F} = \left. {C_{I} + \frac{\partial C}{\partial t}} \middle| {{emission} + \frac{\partial C}{\partial t}} \middle| {{met} + \frac{\partial C}{\partial t}} \middle| {chemistry} \right.} & \left. 1 \right)\end{matrix}$where C_(I) is an initial pollutant concentration. To compute theP-factor as a pollutant comprehensive scavenging ability of theatmosphere, the meteorological scavenging ability component model iscomputed according to:

$\left. \frac{\partial C}{\partial t} \middle| {met} \right. = {{{- \nabla_{H}} \cdot V_{H} \cdot C} + \left\lbrack {\frac{\partial\left( C_{\eta} \right)}{\partial t} - {C\;\frac{\partial^{2}h}{{\partial z}{\partial t}}}} \right\rbrack + {{\nabla{\cdot \rho}}\; K\;{\nabla\left( \frac{C}{\rho} \right)}} + {\sigma\; C}}$$\mspace{20mu}\left. \frac{\partial C}{\partial t} \middle| {{{\left. {met} \right.\sim\alpha_{1}}C} + \beta} \right.$where, in the meteorological scavenging ability component of theP-factor, the −∇_(H)·V_(H)·C term represents XY advection (1^(st)derivative); the

$\left\lbrack {\frac{\partial\left( C_{\eta} \right)}{\partial t} - {C\frac{\partial^{2}h}{{\partial z}{\partial t}}}} \right\rbrack$term represents a Z transport (1^(st) and 2^(nd) derivative), the

${\nabla{\cdot \rho}}\; K{\nabla\left( \frac{C}{\rho} \right)}$term represents diffusion (2^(nd) derivative), and the σC termrepresents a deposition (0 derivative) with V_(H) being the horizontalwind vector, ρ being atmospheric density, K is a turbulent exchange(diffusion) coefficient, η is the net vertical transport rate, and σ isthe removal (including dry deposition and wet scavenging) ratecoefficient.

Further, the chemical scavenging ability component of the P-factor ismodeled according to:

$\left. \frac{\partial C}{\partial t} \middle| {chemistry} \right. = {\alpha_{2}\lbrack C\rbrack}$

In the chemical scavenging ability component of the P-facto the [C] termrepresents a pollutant concentration in chemical reactions (0derivative).

Under certain meteorological and chemical conditions, the P-factorpollutant concentration approximately follows a linear relationship withpollutant concentration, as shown in equation 2):P(C)≈C+β  2)where α and β are key coefficients representing that pollutantconcentration relation.

Once the P-factor is computed, the value is used to determine apurgeable pollutant emission value 78 (FIG. 2). The purgeable pollutantemission value, along with any constraint conditions 80 are consideredto enable calculating an optimal emission scheme 82 by the dynamicemission correction system. In some embodiments, at 82, the optimalemission scheme works to: 1) control a target concentration; and 2) atthe lowest economic cost. The system makes full use of the atmosphericpollutants scavenging ability which could provide the most effective(and lowest economic cost) emission discharge scheme. In someembodiments, the optimal emission scheme 82 takes into accountconstraint conditions 80 from a user, such as a decision or policy makerthat can impact the pollutant emission source.

FIG. 3 shows an example method 100 for generating an emission dischargescheme according to one or more embodiments of the present invention; InFIG. 3, the method 100 includes receiving, at a computer system, variousinputs including: (1) geographic data 102 e.g., that can be downloadedfrom a public web site (including data representing the physical andtopological land formations of the area that is to be emissioncontrolled, e.g., 2D geographical data of an area); and (2) large scalemeteorological data 105, e.g., that can downloaded from a public website which data represents conditions of the atmosphere at that area,e.g., wind, temperature, air density, pressure, relative humidity, fluiddynamics, thermodynamics, radiation, etc. The above two data types 102,105 may be grid format data covering a global scale or areas ofinterest. Emission data 107 can also be input to the system 100, e.g.,information about emissions in a local area, e.g., such as may begenerated by an industry in a city (e.g., SO₂ discharge volume).Emission data for an area may by generated by various sources, such asindustrial point sources, area sources, mobile sources (e.g., road andmarine), and natural sources (e.g., wildfires and biogenic/geogenic).These datum 102, 105 and 107 are input to the system 100, wheresimulations processing 150 generates key coefficients 160 (α and β). Anexemplary generation of coefficients α and β will be described in moredetail with reference to FIG. 4. Simulations processing 150 includes:defining an initial pollutant concentration 120; determining an emissioninfluence on concentration 130 (e.g., a concentration change in theatmosphere caused by emission); computing the P-factor 140; leading tothe determination of the coefficients 160 (α and β). After computing thekey P-factor coefficients, a scavenging forecast model referred to asthe Pollutant Emission Scavenging Forecasting (PESF) model 175 is run todetermine a purgeable pollutant emission value. From results of thisscavenging forecast model, a dynamic emission correction system 180 (anexample of which is described with reference to FIG. 7) is invoked togenerate an optimal emission discharge scheme as a system output at 190.

FIG. 4 shows an example method 150 for generating coefficients based onthe simulations in one or more embodiments of the present invention.Simulations processing 150 particularly includes conducting steps of,for each emissions inventory (which is a list of emission discharge fromdifferent industries in the local area): an air pollution simulationwith a numeric model, using the meteorological conditions 152, and afirst emissions inventory value (which may be an original inventory,e.g., inv. 1) 154A of a first emissions intensity, and using thechemical scheme 156 (i.e., same chemical reactions in the Air QualityNumeric Model), to generate a first P-factor, P1, at a processing block115A. In block 115A, there is determined, for an initial pollutantconcentration value (C_(I1)) 120A, an emission influence onconcentration δC_(emis) at 130A (i.e., the pollutant concentrationchange cause by emission inventory input) and from that value, there isdetermined the P-factor (P1) indicating the pollutant concentrationchange caused by meteorological and chemical processes at 140A.

Given a further emission inventory, e.g., a second method may beperformed in parallel, by one or more processors of a computer system,to determine a P-factor based on a second inventory value, which may behalf the discharge volume of inventory 1 (e.g., inv. 2) 154B of a secondemissions intensity. In the coefficient generating method, under thesame the meteorological conditions 152 and same chemical conditions 156,there is further generated a second P-factor, P1, at a processing block115B. In block 115B, there is determined, for an initial pollutantconcentration value (C_(I2)) 120B, an emission influence onconcentration δ_(Cemiss) at 130B and from that value, determine theP-factor (P2) at 140B.

Continuing to 155, FIG. 4, both generated P-factors P1 and P2 areprocessed at 155 according to equation 2) to generate the keycoefficients. For example, a system of equations is set up for theexample air quality numerical model processing of FIG. 4P1=α·(C _(I1) +δC _(emiss1))+βP2=α·(C _(I2) +δC _(emiss2))+β

In these system of equations, coefficients α, β are determinable airquality numerical model outputs, which include the concentration changefrom different physical and chemical processes. At any time instant,using these coefficients, the key parameters of a PESF model may be fitto determine a purgeable emissions component at any moment in time.

In one embodiment, a PESF model is generated to determine

$\left. \frac{\partial C}{\partial t} \right|$emission at any one or multiple time instances according to equations 1)and 2) according to equation 3) as follows:

$\begin{matrix}{\left. \frac{\partial C}{\partial t} \middle| {emission} \right. = {C_{F} - C_{I} + {P\left( {C_{I} + \frac{\partial C}{\partial t}} \middle| {emission} \right)}}} & \left. 3 \right)\end{matrix}$

From equation 3), the purgeable emissions component E at any moment intime (t=i) is then computed according to equation 4) as follows:

$\begin{matrix}{E\left. _{t = i}{= {C{\int{\int{\int{\left( {\int{\left( {\frac{C_{F} - \beta}{\alpha + 1} - C_{I}} \right){dt}}} \right){dxdydz}}}}}}} \right)} & \left. 4 \right)\end{matrix}$where C_(F) is a target pollutant concentration at any time in theatmosphere.

FIG. 5 depicts a graph of example emissions amounts (e.g., true amounts)periodically over a time period as compared to purgeable emissionamounts at corresponding time instances in one embodiment. As shown inFIG. 5, the graph 170 depicts example measured relatively uniformemissions amounts 174 (e.g., true amounts) over a period, particularlyat each of multiple unit time periods i, e.g., i=1, 2, . . . , 10.Additionally depicted are corresponding purgeable emissions amounts 178(E) computed at each time period i.

FIG. 6 shows a time line 200 depicting the use of the PESF model and keycoefficients to determine the purgeable emissions component E_(t) atsuccessive times t=i−1, i, i+1 in one embodiment. In one embodiment, atime period (delta T) between consecutive purgeable emissions dischargecomponent E_(t) computations is configurable, and may be 1 hour. Forexample, a 2-day discharge plan may be generated with computations ateach hour, e.g., for 48 hours. While periodic computations are shown atsuccessive times t=i−1, i+1, etc., it is not necessary that this beperiodic and the purgeable emissions component calculation may occur anytime. The time line 200 of FIG. 6 reflects that if the emission is lowerthan minimum acceptable value for a current moment (time instance), itbecomes necessary to correct the emission intensity before (i.e., priorto) this current moment by changing the initial concentration at thistime. As shown in the time line 200 of FIG. 6, the initial concentrationthus becomes the final concentration of last time.

Thus, as shown in the time line 200 at each consecutive time t=i−1, iand i+1, there is correspondingly computed corresponding purgeableemissions components E^(i−1), E^(i), and E^(i+1) using equation 4). Inthe computations of E^(i−1) at time i−1, there is computed the initialconcentration C_(I) ^(i−1), a final concentration C_(F) ^(i−1), andcoefficients α^(i−1), β^(i−1). Similarly, for the computations of E^(i)at time i, there is computed the initial concentration C_(I) ^(i), afinal concentration C_(F) ^(i), and coefficients α^(i), β^(i). As shownat 205 in FIG. 6, the final pollutant concentration C_(F) ^(i−1) of theimmediately prior time period i=t−1 is the initialized valueconcentration value C_(I) ^(i) for the next current period. Statedalternatively, C_(I)|_(t=i)=C_(F)|_(t=i−1). Similarly, for thecomputations of E^(i+1) at time i+1, there is computed the initialconcentration C_(I) ^(i+1), a final concentration C_(F) ^(i+1), andcoefficients α^(i+1), β^(i+1). It is similarly noted at 207 that thefinal pollutant concentration of the prior period C_(F) ^(i) becomes theinitialized concentration value C_(I) ^(i+1) of the next period.

FIG. 7 shows an example method 300 for generating an emission correctionscheme according to one or more embodiments of the present invention. Asshown, at step 305, a client specifies, through a computer systeminterface, a target concentration (i.e., a density) of pollutant, and aminimum acceptable emission amount E_(min) (e.g., in tons). In oneembodiment, the user-specified target concentration of pollutant is astandard pollutant concentration level C_(st). At step 310, the time isinitialized as t=1 and a variable FLAG is initialized to zero (i.e.,FLAG=0). A determination is made at 315 whether the current time is lessthan a maximum time NH, i.e., whether t<NH, where NH is the period forthe discharge plan being made, e.g., NH=48 hours in order to make a2-day discharge plan. If time t is greater than NH, then the method endsat 360. Otherwise, at step 315, if the current time is less than maximumtime NH, the process continues to 320 where a final concentration amountat i=t is C_(F) ^(t) is assigned the value of the customer specifiedtarget concentration of pollutant C_(st), i.e., C_(F) ^(t)=C_(st). Then,at 325, the method computes the purgeable pollutant emission at the timei=t, i.e., compute E^(t). Continuing to step 330, a determination isthen made as to whether the current computed purgeable pollutantemission E^(t) is greater than the customer specified minimum acceptableemission amount E_(min), whether E^(t)>E_(min).

If, at step 330, it is determined that the current computed purgeablepollutant emission E^(t) is greater than the specified minimumacceptable emission amount E_(min), then the process continues to 335,FIG. 7 where a further determination is made as to whether the variableFLAG has been assigned a value of one (i.e., is FLAG=1). If FLAG doesnot equal 1, i.e., FLAG≠1, then the process proceeds to 350 where thetime is incremented to the next value, i.e., t=t+1. Afterwards, theprocess returns to 315, FIG. 7 where steps 315, 320, 325 and 330 arerepeated.

Returning to step 335, it is determined that the variable FLAG has beenassigned a value of one (i.e., FLAG=1), then the process proceeds to355, FIG. 7 where the variable FLAG is re-set to value FLAG=0, and thecurrent time t is updated as time TT which time TT represents the firsttime emission volume is less than the minimum (i.e., E<E_(min)). Afterassigning the value TT to current time t, the process proceeds to step350 where the time is incremented to the next value, i.e., t=t+1, andthe process returns to 315 where steps 315, 320, 325 and 330 arerepeated. Thus, for the FLAG variable: if E<E_(min), FLAG=1, ifE>E_(min), FLAG=0.

Returning to step 330, if it determined that the current computedpurgeable pollutant emission E^(t) is not greater than the specifiedminimum acceptable emission amount E_(min), then the process proceeds to340 where: the variable FLAG is assigned a value of one, i.e., FLAG=1,and the variable TT is assigned the current time t to indicate that t(=TT) now represents the first time an emission volume is less than theminimum, and additionally, a value C_(F) ^(t−1) is updated with a valueequal to f(E_(min−)E).

For example, for the step at time t, the original equilibrium is: C_(F)^(t)=C_(I) ^(t)+C_(E) ^(t)+C_(P) ^(t) where C_(F) is the final pollutantconcentration of this time period, e.g., hour (and it should be equal toa target concentration), C_(I) is initial pollutant concentration ofthis hour (and it is equal to a final concentration of an immediateprior hour), C_(E) is pollutant concentration change caused by emission,and C_(P) is the pollutant concentration change caused by meteorologicaland chemistry processes in the atmosphere. This continuous equation isestablished at each time period, e.g., hour. Since E>Emin, so theequilibrium needs to be updated to:

C_(F) ^(t)|_(new)=C_(I) ^(t)|_(new)+C_(E min) ^(t)+C_(P) ^(t)|_(new). Sothe C_(F) ^(t−1) need to be updated to:

$\begin{matrix}{C_{F}^{t - 1} = \left. C_{I}^{t} \right|_{new}} \\{= {\left( C_{F}^{t} \middle| {}_{new}{- C_{F}^{t}} \right) - \left( C_{P}^{t} \middle| {}_{new}{- C_{P}^{t}} \right) - \left( {C_{{Emi}\; n}^{t} - C_{E}^{t}} \right) + C_{I}^{t}}} \\{= {{f\left( {E_{m\; i\; n} - E} \right)}.}}\end{matrix}$

Thus, once C_(E) is calculated, emission volume could be calculated byintegration (i.e., the pugeable emission). If emission volume is lessthan the minimum acceptable emission volume, the emission volume will beforced equal to minimum acceptable emission volume and the C_(I) needsto decrease to ensure the C_(F)<=target concentration. Once C_(I)changes, the C_(F) of last hour changes too, thus the equilibriumrelationship of last hour needs to be revised, i.e., the emission volumeof previous hours should decrease in advance to ensure concentration ofthis hour meet the target.

Then the process continues to step 345 where the time is decremented toa previous value, i.e., t=t−1. Then, the process returns to step 325where the purgeable pollutant emission component E^(t) is again computedand step 330 is repeated.

Thus, in view of FIG. 7 processing, when it can not be ensured that anemission volume at a current time has a pollutant concentration lessthan a target concentration, emission volume needs to be reduced fromprevious days. In such cases, the concentration is higher, and a longerlead time may be needed to meet the target concentration.

FIG. 8 depicts a further time line 250 corresponding to the timeline ofFIG. 6, however representing optimal emissions scheme showing modifiedpurgeable emission components E′^(t) computed at the successive timest=i−1, i, i+1 in one embodiment under the constraints specified.Resulting from invoking the method 300 of FIG. 7, there is generated adynamic emission reduction control shown at example times t=i−1, i, i+1.That is, as shown in the time line 200 at each consecutive time t=i−1, iand i+1, there is correspondingly computed corresponding optimalemissions components E′^(i−1), E′^(i), and E′^(i+1) using equation 4).

Thus, by computing coefficient α, β and using the pollutant emissionscavenging forecasting (PESF) model of equation 3), the methoddetermines the purgeable pollutant emission E′^(t) which could controlpollutant at any target concentration, considering the initial pollutantconcentration and pollutant scavenging ability of atmosphere. In orderto take the constraint conditions of decision makers into consideration,the dynamic emission correction system of FIG. 7 is used to quicklycalculate the optimal emission scheme.

FIG. 9A shows an example case study 400 depicting a line plot of theparticulate matter PM2.5 emission concentration 402 emitted by source ofthe pollutant with no emission control. The pollutant concentration plot402 reflects the true emission concentration levels emitted by thepollutant source at successive (daily) time instances t=1, 2, . . . ,21. Additionally shown is a plot of the computed PM2.5 concentration,i.e., C_(F) ^(t), with purgeable pollutant emission E^(t) 405 andcomputed at the same time instances t=1, 2, . . . , 21. As shown theplot 405 of the computed pollutant concentration based on purgeablepollutant emission E^(t) reflects an example user specified targetconcentration constraint of 75 ug/m³.

FIG. 9B shows for the example case study 400 of FIG. 9A, a bar graphplotting the example corresponding daily true emission levels 412 (e.g.,shown in 10 k tons) emitted by source of the pollutant at the successive(e.g., daily) time instances t=1, 2, . . . , 21. As shown, the dailytrue emission levels are uniform over the time period. Additionallyshown are the computed purgeable pollutant emission levels E^(t) 405computed at the same time instances t=1, 2, . . . , 21. The plot 415 ofthe computed purgeable pollutant emission E^(t) reflects a userspecified minimum emission level 420 of 50,000 tons.

FIG. 9C shows for the example case study 400 of FIG. 9A, a line plot 450of the particulate matter PM2.5 emission concentration 452 emitted usinga dynamic optimal emission scheme such as described herein in connectionwith FIG. 7 for computing purgeable emissions E′^(t) at the successivetime instances. Additionally shown is the plot of the computed PM2.5concentration, i.e., C_(F) ^(t), with purgeable pollutant emission E^(t)405 computed at the same time instances. As shown the plot 455 of thecomputed purgeable pollutant emission concentration E′^(t) reflects anexample user specified target concentration constraint of 75 ug/m³.

FIG. 9D shows for the example plot 450 of FIG. 9C, a bar graph plottingthe optimal emission levels 455 (e.g., E′^(t)) emitted by the source ofthe pollutant at the successive (e.g., daily) time instances t=1, 2, . .. , 21 to achieve the optimal concentration levels 452. Further shownare the computed purgeable pollutant emission levels E^(t) 415 computedat the same time instances t=1, 2, . . . , 21. The graph 455 of thefuture emission volume levels E′^(t) in one embodiment, represents theoptimal discharge scheme and reflects the user specified minimumemission level, e.g., 50,000 tons and specified target concentrationconstraint of 75 ug/m³ 410. In one embodiment, the emission volume iscalculated based on purgeable emission considering the constraintscondition of a particular client or entity. The graphs of FIGS. 9B and9D reflect a dynamic control strategy 460 based on particularenvironmental capacity (e.g., below 75 ug/m³). This example dischargescheme makes full use of atmospheric pollutants scavenging ability, sothis emission control technique is more optimal than extensive control.

In one embodiment, once a reduce discharge plan is determined, based onthe methods of FIG. 7, an entity, e.g., a government of the local area,may decide how to take action, such as, a closing of part of theindustry, or restricting cars based on the odd and even number rule, forexample.

FIG. 1 illustrates an exemplary network-connected system in accordancewith one or more embodiments of the present invention. It is to beunderstood that the computer system depicted is only one example of asuitable processing system and is not intended to suggest any limitationas to the scope of use or functionality of embodiments of the presentinvention. For example, the system shown may be operational withnumerous other general-purpose or special-purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with the system shown in FIG. 1 may include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, handheld or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

In some embodiments, the computer system 12 may be described in thegeneral context of computer system executable instructions, embodied asprogram modules stored in memory 16, being executed by the computersystem. Generally, program modules may include routines, programs,objects, components, logic, data structures, and so on that performparticular tasks and/or implement particular input data and/or datatypes in accordance with the present invention (see e.g., FIG. 3).

The components of the computer system may include, but are not limitedto, one or more processors or processing units 12, a system memory 16,and a bus 14 that operably couples various system components, includingsystem memory 16 to processor 12. The processor 12 may include a module10 that performs the methods described herein. The module 10 may beprogrammed into the integrated circuits of the processor 12, or loadedfrom memory 16, storage device 18, or network 24 or combinationsthereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

The computer system may include a variety of computer system readablemedia. Such media may be any available media that is accessible bycomputer system, and it may include both volatile and non-volatilemedia, removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

The computer system may also communicate with one or more externaldevices 26 such as a keyboard, a pointing device, a display 28, etc.;one or more devices that enable a user to interact with the computersystem; and/or any devices (e.g., network card, modem, etc.) that enablethe computer system to communicate with one or more other computingdevices. Such communication can occur via Input/Output (I/O) interfaces20.

Still yet, the computer system can communicate with one or more networks24 such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter22. As depicted, network adapter 22 communicates with the othercomponents of computer system via bus 14. It should be understood thatalthough not shown, other hardware and/or software components could beused in conjunction with the computer system. Examples include, but arenot limited to: microcode, device drivers, redundant processing units,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allelements in the claims below are intended to include any structure,material, or act for performing the function in combination with otherclaimed elements as specifically claimed. The description of the presentinvention has been presented for purposes of illustration anddescription, but is not intended to be exhaustive or limited to theinvention in the form disclosed. Many modifications and variations willbe apparent to those of ordinary skill in the art without departing fromthe scope and spirit of the invention. The embodiment was chosen anddescribed in order to best explain the principles of the invention andthe practical application, and to enable others of ordinary skill in theart to understand the invention for various embodiments with variousmodifications as are suited to the particular use contemplated.

What is claimed is:
 1. A computer-implemented method of dynamicallycontrolling emission discharge of pollutants by a pollutant emittingsource in a spatial area comprising: receiving at a programmed hardwareprocessor, geographic data, atmospheric meteorological data and chemicaldata pertaining to said spatial area, receiving at the programmedhardware processor, emissions data representing pollutant substancesemitted by a emitting source; generating, by the processor, based onsaid received meteorological and emissions data, a pollutant scavengingability factor representing a pollutant comprehensive scavenging abilityof the atmosphere, said pollutant scavenging ability factor generatedby: specifying one or more emissions inventories, each respectiveemissions inventory specifying a changing emissions amount at saidpollutant emitting source and a respective initial concentration of saidpollutant; and for each emissions inventory: simulating, undermeteorological and chemical conditions, said emissions inventory, todetermine an emissions influencing change of pollutant concentrationunder said meteorological and chemical conditions from said initialconcentration, and generating, for said pollutant scavenging abilityfactor of said emissions inventory, a first coefficient and secondcoefficient, said first coefficient and second coefficient representinga linear relation between said pollutant scavenging ability factor andsaid pollutant concentration of the atmosphere in said area under saidmeteorological and chemical conditions; computing, at the processor, apurgeable pollutant emission value over a period of time using saidscavenging ability factor, said first coefficient and secondcoefficient, and a target pollutant concentration of the atmosphere;receiving, at said processor, one or more emissions constraintconditions; and dynamically controlling emissions at said emittingsource based on said computed purgeable pollutant emission value,wherein controlled pollutant emissions of said emitting source complywith said received one or more emission constraint conditions.
 2. Thecomputer-implemented method of claim 1, wherein said pollutantscavenging ability factor approximately follows a linear relationshipwith a pollutant concentration of the atmosphere in said area.
 3. Thecomputer-implemented method of claim 1, wherein said computing saidpurgeable pollutant emission value comprises: computing a purgeablepollutant emission value of a pollutant as a function of an initialpollutant concentration at each current time instance of successive timeinstances, a final concentration of said pollutant at an immediate timeinstance prior to said current time instance, and said first and secondcoefficients representing said linear relation.
 4. Thecomputer-implemented method of claim 3, wherein an initial concentrationof said pollutant at a current time instant is equal to a finalconcentration of said pollutant at the immediate time instance prior tothe current time instant.
 5. The computer-implemented method of claim 3,further comprising: receiving one user-specified meteorologicalconstraint condition comprising a standard pollutant concentrationlevel, and a user-specified chemical constraint condition comprising aminimum emissions requirement.
 6. The computer-implemented method ofclaim 5, wherein said dynamically controlling emissions at saidpollutant emitter source based on said purgeable pollutant emissionvalue over said time period comprises: calculating, at each current timeinstance of said successive time instances, a computed purgeablepollutant emission value based on said standard pollutant concentrationlevel at each time instance; determining at each said current timeinstance, whether a computed purgeable pollutant emission value is lowerthan said minimum emissions requirement; and for a current time instancehaving a computed purgeable pollutant emission value lower than saidminimum emissions requirement, correcting an emission intensity of saidpollutant emitting source.